Published byStanford Medicine

Google’s PageRank algorithm sorts search results by relevance and now researchers are using a similar strategy to sift through thousands of proteins that affect the progression of pancreatic cancer.

German scientists from the Dresden University of Technology ranked cancer biomarkers and found seven proteins that predicted how patients respond to chemotherapy. The study was published in PLoS Computational Biology.

Cancer biomarkers have garnered considerable interest in medical and clinical research. They could be used to predict the outcomes of individuals with cancer and personalize therapy. But so far, few biomarkers have proved clinically useful. For example, controversy surrounds the effectiveness of measuring levels of prostate-specific antigen (PSA) as a way of screening prostate cancer; PSA levels are also high in non-cancerous enlarged prostates. In addition, biomarkers identified in different studies almost never overlap.

The German team worked around this problem by analyzing relationships between biomarkers. An article from e! Science News explains:

This problem has been circumvented using the Google strategy, which takes into account the content of a web page and also how these pages are connected via hyperlinks. With this strategy as the model, the authors made use of the fact that proteins in a cell are connected through a network of physical and regulatory interactions; the ‘protein Facebook’ so to speak.

“Once we added the network information in our analysis, our biomarkers became more reproducible,” said Christof Winter, the paper’s first author. Using this network information and the Google Algorithm, a significant overlap was found with an earlier study from the University of North Carolina. There, a connection was made with a protein which can assess aggressiveness in pancreatic cancer.

The group is currently running a clinical trial to evaluate these new biomarkers.

Using signal-from-noise algorithms from other sciences to accelerate biological progress is a fantastic potential. More interdisciplinary work may prove more parallels where insights can be leveraged. But career-wise – academic science does not reward those who straddle between digital and biological.